Multi-source information gain for random forest: An application to CT image prediction from MRI data

Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of multi-source information gain that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing multiple beneficial sources of information, instead of the sole governing of prediction targets as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the multi-source information gain concept effectively helps better guide the training process with consistent improvement in prediction accuracy.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
EditorsLuping Zhou, Yinghuan Shi, Li Wang, Qian Wang
PublisherSpringer Verlag
Pages321-329
Number of pages9
ISBN (Print)9783319248875
DOIs
Publication statusPublished - 2015
Event6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: 2015 Oct 52015 Oct 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9352
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period15/10/515/10/5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2015). Multi-source information gain for random forest: An application to CT image prediction from MRI data. In L. Zhou, Y. Shi, L. Wang, & Q. Wang (Eds.), Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings (pp. 321-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352). Springer Verlag. https://doi.org/10.1007/978-3-319-24888-2_39